Tensile Strength of Orbital Welded Mild Steel Tubes with Dissimilar Thickness

Hussein, Nur Izan Syahriah; Ayof, Mohamad Nizam; Nordin, S.

doi:10.7763/ijmmm.2016.v4.225

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…Welding of pipes and tubes is employed in practically every engineering application. As a result, the quality of pipeline welding has a direct impact on public safety and product quality [2]. This increase in pipe welding requirements will require a significant investment in machinery production.…”

Section: Introductionmentioning

confidence: 99%

Development of Automatic Orbital Pipe MIG Welding System and Process Parameters Optimization of AISI 1020 Mild Steel Pipe Using Hybrid Artificial Neural Network and Genetic Algorithm

Mengistie¹,

Bogale

2023

Preprint

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Currently, in pipe welding, it is nearly difficult for a human welder to weld the whole circumference of a pipe in a single uninterrupted pass using MIG welding causing inconsistencies in weld quality around the welded pipe. The aim of this study was to develop an automated-orbital pipe MIG welding system and to optimize welding parameters for enhancing ultimate tensile strength and Rockwell hardness of mild steel 1020 grade pipe. Three levels of variation were applied to the four input parameters that were chosen. Nine experiments were carried out using orthogonal array of L9. In this experimental investigation, the highest ultimate tensile strength (UTS) of 411.2 MPa and Rockwell hardness (RH) of 95 HRB were achieved at 110 A of current and 24 V of voltage, welding gun travel speed of 30 cm/min and 3 mm of arc length. For modeling the orbital pipe MIG welding process experimental input parameters and response results, a hybrid ANN-GA model was constructed. This model was used to forecast and optimize ultimate tensile strength and Rockwell hardness, as well as the process factors that go with it. The results indicated that the ANN-GA model could predict the output responses with a mean absolute error of 5.06e-05. During optimization, a 4-9-2 network trained with neural network of back propagation by Bayesian regularization approach was determined to have the greatest prediction capability, with maximum UTS and RH of 417.857 MPa and 96.5364 HRB, respectively. The predicted and confirmation test results were both found within the acceptable errors, according to a confirmation test conducted with the optimum parameters of the ANN-GA model.

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Section: Introductionmentioning

confidence: 99%